1. Field of the Invention
The present invention relates generally to digital photography, and more particularly, to the removal of noise in a digital photograph.
2. Description of the Related Art
Mobile phone cameras have improved greatly in terms of captured image resolution. However, the form of the mobile phone, which enables mobility, places limitations on the optics that can be incorporated into the mobile phone cameras. Also, the Complementary Metal Oxide Semiconductor (CMOS) sensor array density increases with increased resolution, which can lead to increased noise in the image under certain lighting/illumination conditions, such as, for example, in candle-lit environments.
A flash has been utilized to aid in the capture of high-quality photographs in low-light conditions. Recent advancements have enabled a Light Emitting Diode (LED) flash to be incorporated in mobile phone cameras. While the range (i.e., the effective distance) of an LED flash is considerably limited when compared to the conventional flash (i.e., Xenon flash) that is found in digital cameras and camcorders, it is often sufficient for capturing photographs in small gatherings.
However, the presence of a camera flash presents a different problem in digital capture. While the flash enables a high quality photo to be captured, it nullifies the effect of ambience in the captured image. For example, when capturing an image of a restaurant table, with soft, yellow ambient lighting, and a birthday cake with lit candles, the mobile phone camera would typically yield a noisy and/or blurred image. If the photograph is captured with the LED flash, the quality would be higher, but the ambience (i.e., the original lighting of the environment) is destroyed.
A number of image processing solutions have been employed for improving the quality of the images so as to reduce the effects of noise in the image. Some methods employ image data from a single image to estimate filter weights. These weights are applied to filter the image and reduce the noise in the image. However, the estimated filter weights are computed from noisy image data. For low ambient illumination, this estimate can be erroneous, since the image has a low overall contrast. Thus, gradient-based operations will not be very reliable.
Other noise-reduction methods generally make assumptions about the statistical properties of the noise affecting the images. For example, these methods assume correlation between the noise in two images or within parts of the same image. As such, the extent of noise reduction is shown to depend on the correlation of the noise in the two images. When the correlation coefficient is above a certain threshold, the method is fairly effective. However, when the assumptions about the statistical properties do not hold, the method will not be effective.
The noise-reduction methods also employ different filtering techniques to minimize the noise in the image. A bilateral filter is commonly used to reduce the noise in the image. Generally, a single image bilateral filter is employed. The original bilateral filter is used as an edge-preserving smoothing filter and is similar to the simple, single-image mean-shift filter. The difference between the bilateral and mean-shift filters is in the use of local information. Specifically, while bilateral filtering uses a fixed, static window, in mean-shift filtering, information beyond the individual windows is also taken into account. Thus, the mean-shift filter performs better at edge-preserving smoothing as compared to the bilateral filter. The same limitation of static, fixed windows also applies to the Joint or Cross bilateral filter.